研究基于波形变换和YOLOv8的乳腺癌病理图像分类方法
1Department of Mathematics and Statistics, Northeast Petroleum University, Daqing, China.
Journal of X-ray science and technology
|January 8, 2024
概括
这项研究引入了一种使用波形变换的新深度学习方法,以改善乳腺癌病理图像分类. 这种新的方法增强了图像特征,提高了计算机辅助诊断系统的准确性.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 计算机科学 计算机科学
背景情况:
- 乳腺癌是一个重大的全球健康挑战,患病率和死亡率很高.
- 深度学习的进步正在彻底改变计算机辅助诊断,特别是在病理图像分析方面.
- 卷积神经网络 (CNN) 越来越多地取代了在医学成像中自动提取特征的传统方法.
研究的目的:
- 提出一种基于深度学习的新方法,用于对乳腺癌病理图像进行分类.
- 通过改进图像分类来提高乳腺癌诊断的准确性.
- 评估将波形变换与用于病理图像分析的深度学习模型相结合的有效性.
主要方法:
- 利用图像翻转进行数据增强以扩展数据集.
- 应用了两级波段分解和重新配置,用于图像利和增强.
- 采用YOLOv8网络模型对乳腺癌病理图像进行八类分类.
- 将处理的数据集分为培训 (80%) 和测试 (20%或30%) 集.
主要成果:
- 拟议的方法,将波形变换与YOLOv8集成,与在原始数据集上使用YOLOv8相比,证明了更好的分类准确性.
- 在不同放大度的图像中观察到分类准确度的提高.
- 波形分解和YOLOv8的组合在分类乳腺癌病理图像方面被证明是有效的.
结论:
- 这种新方法有效地提高了乳腺癌病理图像的分类.
- 将两级波纹分解和重新配置与YOLOv8网络模型相结合,为计算机辅助乳腺癌诊断提供了一个有前途的方法.
- 这种技术显示了提高乳腺癌检测诊断准确性的潜力.
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